Design and Implementation of Cognitive Assessment Tool for Working Memory and Attention based on PGI Memory Scale
Bibliographic record
Abstract
Cognitive function is one of the most fundamental psychological functions that play a significant role in person’s daily life. Impairment in cognitive function can impacts the daily functioning and overall performance of the person. A digital application could be an accessible and convenient method for the effective evaluation of cognition. The proposed Cognitive Assessment Digital Smart Tool (CADST) evaluates the Attention (ATT) and Working Memory (WM) parameters of cognition. The outcome measures of CADST were evaluated against PGI Memory Scale (PGIMS) and Montreal Cognitive Assessment (MoCA). Usability testing for the CADST tool was performed using the Post‒Study System Usability Questionnaire (PSSUQ). A total of 30 healthy participants were recruited (women = 12, men = 18; age (M ± SD) = 35.6 ± 10.63 y. o.). The feasibility study analysis revealed a significant moderate to strong correlation between the total scores of CADST and PGIMS (r = 0.75; p < 0.001) and a low to moderate correlation between the total scores of CADST and MoCA (r = 0.44; p < 0.001). Subtests of CADST and PGIMS showed strong correlation for ATT (r = 0.81; p < 0.001) and moderate correlation for WM (r = 0.51; p < 0.001). Similarly, subtests of CADST and MoCA showed moderate correlation for ATT (r = 0.63; p < 0.001) and low correlation for WM (r = 0.24; p = 1.82). CADST showed a high correlation with PGIMS for evaluating ATT and WM symptoms of cognition provide evidence of convergent validity. CADST is the first digital smart screening tool based on PGIMS for ATT and WM using web‒based technology. The overall usability ratings showed high acceptance for system usage, interface and information quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".